Abstract

It is widely held that the methods of AI are the appropriate methods
for cognitive science. Fodor, however, has argued that AI bears
the same relation to psychology as Disneyland does to physics.
This claim is examined in light of the widespread but paradoxical
acceptance of the Turing Test--a behavioral criterion of intelligence--among
advocates of cognitivism. It is argued that, given the recalcitrance
of certain deep conceptual problems in psychology, and disagreements
concerning psychology's basic vocabulary, it is unlikely that
AI will prove to be very psychologically enlightening until after
some consensus on ontological issues in psychology is achieved.

1. Introduction

It is widely held that the methods of artificial intelligence
(AI) constitute a legitimate--even the preferred--manner of conducting
research in cognitive science. This belief is due, in large part,
to the primacy of computational functionalism (CF), currently
the most influential framework for cognitive science. Functionalism--whether
of the computational variety or not--holds that mental states
are abstract functions that get one from a given input (e.g.,
sensation, thought) to a given output (e.g., thought, behavior).
Thus, under CF, a mental state is thought to be nothing more than
a functional role between certain inputs and outputs, or between
these and other functional roles, similarly defined. In this bald
state, however, functionalism is not an adequate account of anything.
As Fodor (1981a) has pointed out, if the only requirement were
the mapping of inputs on to outputs there would be no explanatory
value in functionalism because the task could be trivially accomplished.
For instance, if I wanted to explain how it is that people can
answer questions on a wide array of topics, I could simply postulate
the existence of a "question-answering function" which
just maps all questions on to their answers. Such an explanation
would, of course, be completely vacuous. In order to make functionalism
function, so to speak, one must put constraints on the
sorts of functions that are considered to be reasonable psychological
theories. The computational answer to this question is to allow
only those functions that can be implemented on Turing machines.
A Turing machine is not a machine at all in the usual sense of
the term but, rather, an idealized model of a machine that can
read, write, and erase a finite number of symbol types situated
in a specified order on a (potentially infinite) length of tape.
The machine "decides" which parts of the tape are to
be read, written on, or erased according to rules given in a Turing
machine table. Such tables are, in effect, (very) low level computer
programs. By restricting one's functions to those implementable
on a Turing machine, one rules out the positing of "question-answering
functions", and the like, unless one can specify a
machine table that would enable a Turing machine to actually accomplish
the task at hand. Of course, one could propose other sorts of
functionalisms (logical behaviorism, for instance, was just such
an alternative sort of functionalism; see Fodor, 1981b, pp. 9-10)
but the computer model seems to have the upper hand these days,
partly because it lends itself to realism about mental states,
whereas behaviorism encourages mental instrumentalism, or even
eliminativsm.

It is often argued that if one is a committed CF-ist, for each
cognitive process one wants to investigate, one must develop a
computer program (or at least show how one might, in principle,
be developed) that can replicate the mental process in question.
By hitching one's functionalism to one's computer one keeps the
bogeyman of vacuous circularity at bay. Because such programs
are simply instances of (or, at least, attempts at) AI, AI becomes
the obvious method of choice for researchers in cognitive science.

Among advocates of computational functionalism, Jerry Fodor is
among the best-known and most influential. In a recent paper,
however, he rejected AI as a creditable method of cognitive-scientific
research. The context was one in which Dennett (1991a, p. 91)
accused Fodor of suggesting the whole enterprise of "Good
Old Fashioned AI" (GOFAI) to be ill-founded. Such a suggestion,
Dennett argued, is inconsistent with the main tenets of CF. If
so, it would be incoherent of Fodor to be pro-CF and anti-AI.
In response Fodor wrote:

...in a sense I do believe that the "whole enterprise of
GOFAI is ill-founded". Not because it's got the wrong picture
of the mind, however, but because it has a bad methodology for
turning that picture into science. I don't think you do the science
of complex phenomena by attempting to model gross observable variance.
Physics, for example, is not the attempt to construct a machine
that would be indistinguishable from the real world for the length
of a conversation. We do not think of Disneyland as a major scientific
achievement. (Fodor, 1991, p. 279)

It is the main aim of this paper to examine Fodor's claim against
AI, in light of his advocacy of CF. I will argue that there are,
indeed, several interesting parallels of the sort Fodor suggests,
especially when AI is looked at in terms of its most widely celebrated
criterion for success, the Turing test. More importantly, whereas
there has long been consensus on what physics is supposed to explain,
and what entities are to be included in those explanations, there
is little such consensus in psychology and, thus, a successful
cognitive Disneyland would probably be even less useful
to psychology than a physical Disneyland would be to physics.

To outline the argument a little more fully, after dispensing
with some likely misinterpretations of Fodor's central claim,
I try to explicate the Disneyland analogy, showing that the question
of how "realistic" a given Disneyland is depends, in
part, on whether its evaluators conform with certain a priori
criteria of "normal" action. I then show that precisely
analogous issues come into play when considering the validity
of the Turing Test, thereby lending credence to Fodor's analogy.
Next, I argue that the very ability to distinguish between Disneyland
and the real world depends to a significant degree on one's prior
scientific understanding of worldly phenomena. Since, on any reasonably
realistic reckoning, we do not have a very good idea of what the
determinants of real psychological states and processes are, it
follows that cognitive scientists may well be in a significantly
worse position vis-a-vis distinguishing the real from the
ersatz than is a physicist investigating Disneyland. I go on to
suggest that, in the absence of such knowledge, AI seems to have
implicitly turned to a strategy reminiscent of one that has been
used by those 20th century semanticists wanting to rid themselves
of the troublesome problem of reference; a strategy of especially
dubious value in an empirical setting such as cognitive science.
Finally, I suggest that the diverse ontological commitments of
cognitive scientists make it very unlikely that the outcome of
any AI research, as it is currently conducted, will prove compelling
enough to lead to a reasonable consensus about the nature of mental
states or processes, and that work of the type practiced by Fodor
is, at this point in cognitive science's history, far more likely
to be significantly fruitful.

2. What Not to Think about Fodor

The first task is to dispel some likely misinterpretations of
the Disneyland quotation above. One might think that because Fodor
specifically cites GOFAI, that he is exempting connectionist AI
from his argument. Nothing could be further from the truth. Fodor
is a fierce detractor of connectionist models of mind. With Zenon
Pylyshyn (1988) he has argued that connectionist models do not--or,
more precisely, are not inherently constrained to--exhibit
the productivity, generativity, and systematicity characteristic
of many mental activities, most notably language. In fact, with
regard to the ongoing debate with his primary connectionist adversary,
Paul Smolensky, Fodor says,

As far as I can tell, the argument has gone like this: Fodor and
Pylyshyn claimed that you can't produce a connectionist theory
of systematicity. Smolensky then replied by not producing a connectionist
theory of systematicity. Who could have foreseen so cunning a
rejoinder? (Fodor, 1991, p. 279)

Thus, it cannot be claimed that Fodor's critique of GOFAI was
intended to let connectionism off the hook. It was a response
to a question specifically about GOFAI.

A second possible misconstrual is that Fodor is rejecting the
"strong" view of computational functionalism. According
to John Searle (1980, 1984), the thesis of "strong"
AI is that symbol manipulation of a syntactic sort just is
a cognitive process. Searle is pretty clear about who he has in
mind when he discusses "strong" AI. He lists the giants
of the field: Allen ("we have discovered that intelligence
is just symbol manipulation") Newell, Herbert ("we already
have machines that can literally think") Simon, Marvin ("we'll
be lucky if the next generation of computers keep us around as
house pets") Minsky, and John ("thermostats have beliefs")
McCarthy (all quotations cited in Searle, 1984, pp. 29-30). In
personal communications, Searle has said that early Putnam, as
well as Fodor and Pylyshyn, are to be included in the "strong"
AI group as well. By contrast, under the "weak" view
of AI, programs are just simulations of cognitive processes,
rather than actual instantiations of them. They bear exactly
the same relation to psychology that computational simulations
of weather systems bear to meteorology. No one for a moment believes
them to be actual weather systems.

There is an interesting terminological ambiguity at work here
that is crucial to the present issue. Searle speaks of "strong"
and "weak" AI and puts Fodor in the "strong"
camp. Fodor, however, rejects AI, but subscribes to what might
be called "strong" CF. According to "strong"
CF--at least according to Fodor--the "right kind" of
computational symbol manipulation is thought to actually instantiate
a cognitive process, but it is doubtful that Fodor would be so
liberal as to attribute true cognitive capacity to just any
such computation (as, at least, Simon and McCarthy seem to). What
counts as the "right kind" of computation, however,
is never made very clear, or at least has not been fully worked
out as yet (though Pylyshyn's (1984) notion of "functional
equivalence" seems to be generally accepted, if not entirely
clear). Thus, there is nothing in the "Disneyland" quotation
given above to indicate that Fodor has had a crucial change of
heart on the question of the relation between computation and
cognition. He is still a full-fledged computationalist. He just
doubts that the tools of program design will settle the important
questions that cognitive science is faced with (e.g., How
are the rules and results of cognition represented? What are intentionality
and rationality? What, if anything, are consciousness and qualia?).

A related misunderstanding to be avoided is that it is being claimed
that computers have no part in psychological theorizing at all.
What we have here is really a debate among advocates of
CF: Simon, Newell, Minsky, McCarthy, and others, on the one hand,
advocating "strong" AI as the best way of advancing
CF; Fodor, on the other hand, rejecting "strong" AI
as a productive strategy for advancing CF. Many would say that
even if CF is entirely wrong, there is a place for computational
work in psychology. Formalizing psychological theories in the
form of computer programs assists us in evading certain problems
that have been nearly endemic in the psychological theorizing
of the past. Primarily, computational formalization forces the
theorist to be utterly explicit about exactly what is being claimed
by each premise of the theory. In the past, it has often been
the case that psychological theories relied so heavily on pre-theoretic
psychological intuitions, that it has turned out that theorists
were implicitly assuming the very things they were attempting
to explain. The requirement of computational formalization of
theories compels us to be less sanguine about this possibility,
and catches many instances of it before a given theory even makes
it to press.

While this is all true, I think it bears little on the present
debate. As mentioned above, if "weak" AI is all that
is claimed, then the claim doesn't carry much psychological "punch".
Under "weak" AI, the computer is no more intrinsic to
psychological theory than it is to meteorological theory; it is
just a tool, of no more special importance to the theory than
the pencil with which one writes one's theoretical notes (though
with the useful capacity, noted just above, that pencils lack).
The question at hand, however, concerns whether, among those
who believe the computer to be essential to psychological theory,
one should choose to pursue the goal via computer programming,
or via conceptual analysis (both, of course, including liberal
sprinklings of empirical fact along the way). This is a separate
matter entirely, and the one on which this paper is focused.

3. Is AI Disneyland?

With this detritus cleared out of the way, the primary question
that remains is whether or not, as Fodor claims, AI bears the
same relation to psychology as Disneyland does to physics.

3.1. What is Disneyland?

At Disneyland, roughly speaking, various mechanical contrivances
are put in place out of view that cause the apparent
ground, the apparent river, the apparent plant,
the apparent animal, the apparent person, etc. to
function as one would expect the real McCoy to. Two considerations
that might be called Disney Principles are of particular note
here. First, there are no real grounds, rivers, plants,
animals, or people involved (at least in the parts with which
we are here concerned). Second, for the most part, none of them
are very convincing, except to the very young. So we have to imagine
a sort of Ideal Disneyland--perhaps something more akin to Westworld,
of cinematic fame--in which the features of the ground, rivers,
animals, people, etc. are indistinguishable from the real
thing.

Here we come to the first difficulty. Indistinguishability may
seem to be a pretty straightforward concept, but it is not. What
counts as indistinguishable necessarily depends on an implicit
set of constraints about what sorts of investigations one is allowed
to pursue. There is no "pure" indistinguishability,
as it were. If one digs (deep enough) into the ersatz ground,
one finds, among other things, the machine that causes the simulated
earthquake. If one traces the ersatz river to its source, one
finds a (probably very big) faucet. If one cuts open one of the
ersatz plants or animals or ersatz people, one finds (perhaps)
a mass of electronic circuitry rather than flesh and organs. One
might ask why there cannot be "pure" indistinguishability
in Ideal Disneyland, but to have such would violate the first
Disney Principle given above; viz., that there are no real
grounds, rivers, plants, animals, people, etc. I take it that
one of the implications of Leibniz's Law is that if the ersatz
entities were completely indistinguishable from real ones,
then they would be real ones as well, and, by implication, that
this land would not be Disneyland but, rather, part of Anaheim,
CA itself. So, we are forced to say that only within certain
constraints of normal action (where normal does not include
digging too deep, following the river too far, or doing violence
to the local critters) the Ideal Disneyland is indistinguishable
from the real world.

3.2. Parallels with the Turing test.

One might be led by such considerations to ask, "if AI is
to psychology as Disneyland is to physics, then what part of the
AI-psychology relation corresponds to the constraints placed on
the actions of the investigator of Ideal Disneyland?" The
question is pertinent, and leads us to consider Fodor's claim
that the hypothetical machine, "would be indistinguishable
from the real world for the length of a conversation"
(italics added). The implicit reference is clearly to the Turing
Test (Turing, 1950). To review, the Turing Test is supposed to
distinguish between intelligent and unintelligent computer programs
by pitting them, in conversation with a human interrogator, against
another human. Turing argued that, if the interrogator is unable
to tell which of the interrogatees is the human and which is the
computer, then there is no reason to deny full, actual intelligence
to the program being tested.

Interestingly, in support of Fodor's comparison, Turing put constraints,
parallel to those discussed above with reference to Disneyland,
on the powers of his interrogator to decide which of the entities
with which he is conversing is human, and which a computer. Namely,
he stipulated that the conversation must take place remotely,
via teletype, to prevent the investigative game being played from
being "given away" by the merely physical, as opposed
to the cognitive, characteristics of the participants.

It is nothing short of paradoxical that the cutting edge of cognitivism--AI--would
adopt so stringently behaviorist a test for their success. On
at least one popular account of what happened in psychology in
the 1950s and 1960s, behaviorists such as Skinner said, "There
is no point in looking inside the black box. You will find nothing
there of value," to which the incipient cognitivists (including
the "Artificial Intelligentsia") replied, "Nonsense.
To the degree that we understand the contents of the black box,
we will understand the real determinants of behavior rather
than just the statistical abstractions offered by S-R psychology."

When it came time to judge whether or not AI-ists had succeeded
in producing AI, however, they resorted to the behaviorist tactic
of restricting access to the inside of the black box, under the
pretext that such access would give up the game. Unfortunately,
however, such a restriction has other undesirable implications.
If I, playing the role of Turing's interrogator, am unable to
distinguish between the computer and the human, it may not be
because the two are indistinguishable in principle, but
rather because I don't know what questions to ask. Access to the
innards of the machine might well give me crucial clues as to
how I might be able show, as behaviorally as you like, the computer
to be fraud. Not only is such access legitimate; it is in the
very tradition that allowed cognitivism itself to bring down behaviorism.
Cognitivism cannot deny such access, on pain of something very
much like incoherence.

Turing's restriction was, I believe, not intended to prevent access
to crucial information. It was motivated by the suspicion that
once people know which of the two interrogatees is the machine,
they will be biased toward accepting evidence of its un-intelligence
that they would not find so decisive if kept behind a "veil
of ignorance". In short, the device was intended to keep
the players honest in exactly the same way that Rawls' (1971)
"veil of ignorance" was intended to keep his players
of the "justice game" honest. All other relevant knowledge,
however, is permissible, and must, in principle, be accessible.

The question, then, reduces to one of how the relevant information--information
that might show the computer to be a cognitive fraud--can be gotten
to the interrogator without telling him or her which interrogatee
is the machine. One way to accomplish this would be to let another
person--an assistant interrogator, so to speak--climb into the
machine, find out what question would likely trip it up, and tell
the interrogator to ask that question, but not anything about
the reasons for asking it. In this way, the veil of ignorance
has been maintained; the interrogator does not know which of the
interrogatees is a computer. The crucial information, however,
in the encrypted form of a question to be asked, is put into play.
If, from the answer to the question, the interrogator is able
to tell which of the interrogatees is the human and which the
computer, then the program fails the test, but the "veil
of ignorance" under which Turing wished the interrogator
to operate has been left intact.

3.3. The interpretation of scientific discoveries.

I have, thus far, tried to explicate the comparison of AI to Disneyland,
and the comparison seems to be borne out, but there are still
other serious discrepancies between Ideal Disneyland and AI. One
has to do with how scientific discoveries are interpreted. As
described above, in order to show that Ideal Disneyland is a fraud,
I could dig into the ground and expose the device that causes
the simulated earthquake. In order to make my discovery a meaningful
piece of data, however, I would have to compare my discovery
to my prior beliefs about the causes of real earthquakes.
If I had no knowledge of the geological determinants of real earthquakes,
I might well dig into the Disney ground, find the relevant device,
and conclude that real earthquakes are caused by such devices
as well; i.e., that Disney quakes are real quakes.
This is effectively what the Turing test interrogator is asked
to conclude if the machine, in fact, passes the test. Contrary
to the situation with earthquakes, however, we just don't know,
in large part, what conditions are necessary and sufficient to
the instantiation of real cognitive states and processes. Thus,
when we find a certain piece of machinery underlying an artificial
cognitive process, we don't know whether it shows the machine
to be a fraud, or counts as a bona fide discovery about
cognition.

Rather than digging down into a ready-built machine, however,
AI-ists build them up from scratch, attempting to engineer them
so that they will behave in certain ways we know naturally cognitive
entities to behave (cognitive neuroscientists may be said to do
the former). When they succeed, they conclude that they have found
the architecture of real minds. The question is whether this inference
is justified. I think the closeted assumption underlying this
strategy is one that is borrowed--knowingly or unknowingly--from
conceptual-role theories of semantics. There is an implicit belief
that there is only one way a formal system could be if it were
to be able to exhibit a specific, but indefinitely large, set
of features. If so, any changes to the "right" system
would either reduce the size of the set of data captured by it,
or would be isomorphic to the original one.

John Haugeland (1985) gives the following example. If I give you
only a couple of short letter strings and ask you to decode them
(i.e., translate them into English), you are going to have
a very difficult time because of the indeterminacies involved.
You may well find two, three, or many more equally plausible alternative
decodings, and have no way to choose among them. What you need
are more strings in the same code. These will allow you to falsify
some of the various hypotheses consistent with the initial set
of strings. For instance, imagine that you are given the strings
"clb" and "fjb". You would likely surmise
that the last letter of both is the same. But exactly what letter
it is is impossible to tell. All you know about the other two
letters in each string is that none of them are the same. Thus,
your decoding hypothesis would include all pairs of three-letter
words that end with the same letter, and have no other identical
letters among them; quite a large set.

Next imagine that the strings "bzb" and "czc"
were added to your decoding set. With these two extra examples
you would be able to rule out many hypotheses consistent with
the first two. You now know that whatever "b" represents
can begin a word as well as end it (assuming it represents the
same thing in both cases), and that whatever "c" represents
can end a word as well as begin it (ditto). Your knowledge of
English might also lead you to suspect (though not decisively
conclude) that "z" represents a vowel. Given more strings,
so goes the story, you would be able to narrow down the possibilities
to a precious few, and ultimately to a unique one.

Belief in a similar story, I think, implicitly guides AI research.
There are endless programs that will get a computer to produce
a single grammatical English sentence. By parity of argument,
there must be far fewer that will enable it to produce 100 grammatical
English sentences. And, ultimately, there must only be one that
can enable it to produce any one of the infinitely many grammatical
sentences of the English language. If you find that one program,
so the assumption goes, you must have the program which guides
our production of English sentences. This is, I think,
the AI researcher's implicit belief.

John Searle (1992) has characterized the assumptions of the Artificial
Intelligentsia somewhat similarly. He writes:

The idea [in computational cognitive science research], typically,
is to program a commercial computer so that it simulates some
cognitive capacity, such as vision or language. Then, if we get
a good simulation ... we hypothesize that the brain computer is
running the same program as the commercial computer... (p. 217)

As Searle goes on to point out, however, there are some serious
difficulties with this approach:

Two things ought to worry us immediately about this project. First
we would never accept this mode of explanation for any function
of the brain where we actually understood how it worked at the
neurobiological level [e.g., frog vision]. Second, we would
not accept it for other sorts of system that we can simulate computationally
[e.g., Can word processors be said to "explain"
the functioning of typewriters?]. (p. 217)

To bring this discussion to bear on the main point, the AI researcher,
in effect, just builds cognitive Disneyland after Disneyland,
attempting to capture more and more cognitive "behavior"
with each new model, in the hope that eventually one will capture
it all. The plan is that the one that does so will be declared
the correct one. If one could ever capture all cognitive behavior,
even in a given domain of cognition, this might be a reasonable
plan. On the contrary, however, one can never be sure of capturing
all such behavior because it is an infinitely large set.
On the critique I am developing here, the capturing of any finite
subset would not be enough because, just as infinitely many theories
capture any finite set of data, so infinitely many programs can
be written to produce any finite set of outputs. (This argument
is, incidentally, precisely analogous to the one Chomsky has directed
against advocates of traditional empiricist theories of language
acquisition.)

This critique is very strong. In fact, it is too strong
because it bears against the whole of science, not just against
AI-based cognitive science. There are infinitely many physical
phenomena, for instance, to be captured by the laws of physics,
but this does not prevent us from developing theories that we
hold, even if tentatively, to be true. Perhaps, then, AI can exploit
this similarity to save itself from Fodor's critique, if we consider
AI programs to be nothing more and nothing less than automated
theories of cognition. Recall that this is exactly the
claim of weak AI, which does not indulge in the additional assumption
of strong AI that the "right" program will somehow rise
up out of the realm of the artificial and become a real
mind. Note that Fodor himself believes the additional claim of
strong AI, although he doesn't believe that writing programs is
the right way to find the "right" program. If AI programs
are considered to be just theories of cognition then it might
be plausibly argued that AI is following exactly the same scientific
course as physics; it's just a few centuries behind. In a superficial
way, this may be so; but in what I think is a deeper way, the
problem is far more profound. I think that this analogy between
AI and physics does not hold. I explain this in the next section.

3.4. Are computer programs scientific theories?

Up until the last three paragraphs, I had stuck pretty closely
to discussing Fodor's claim that AI is just Disneyland for cognitivists.
I have now moved a little beyond those confines to a more general
ontological question. As mentioned above, it is believed by many
that computer programs in AI are nothing more or less than scientific
theories of cognition. I will not dispute the point in principle,
but I believe that they have historically not been particularly
good theories in that they have not had the explanatory or predictive
characteristics of good scientific theories. One thing about theories
in physics, and other developed sciences, is that they have very
broad scope. Consider Newton's laws of motion: they cover the
movement of everything from baseballs thrown by little boys to
planets orbiting distant stars. Darwin's theory of evolution covers
the genesis of everything from flu germs and bread mold to Douglas
firs and elephants. By comparison, AI-based cognitive theories
typically cover a very small range of mental phenomena: the perceptual
identification of pictures, or the solving of a single class of
reasoning problems, or the latencies in reading words that occur
with different frequencies in the language. The most explicit
attempt to break out of this tradition--Newell, Simon, and Shaw's
General Problem Solver--has become most notable for having
not been particularly general in its ability to solve problems.
The micro-worlds tradition of the 1970s can be seen as an unconditional
surrender to the problem of narrow theoretical scope. Although
micro-worlds are now a thing of the past, the faith that solutions
to small problems might somehow ultimately "scale up"
is still very much with us.

At the risk of seeming inconsistent, I see no reason to believe
that, in principle, AI programs might one day serve as good models
(as opposed the theories) of cognition; i.e., working instantiations
of principles of cognition that we have discovered by other means.
What I don't see is how such programs will figure significantly
in getting us to that day. I do not believe that the explanation
of intentionality, for instance, will come from a fancy program.
The program that instantiates intentionality (if there is ever
to be one) will, conversely, be developed on the basis of an analysis
(still lacking) of what intentionality is, and how it can arise
from an apparently non-intentional hunk of matter such as a brain
or a computer.

The question that troubles us all--pro-AI and anti-AI alike--is
why we in cognitive science seem to have made so little
progress in answering such questions. The reason, I think, lies,
at least in part, in questions of ontology. Physics, chemistry,
and biology have developed widely accepted sets of entities that
their theories quantify over in a way that psychology does not.
To put it a little crudely, the difference is that physics knows
what it is talking about--e.g., pendula and falling bodies--and
to the extent that it captures the behavior of these entities
with its laws, physics works. Psychology does not, in this sense,
know what it is talking about. The debate still rages over whether
thoughts--presumably at least one of the ground-level entities
of psychology--are to be regarded as propositional attitudes,
whether they belongs in our ontological vocabulary at all, and
if so, how they are related to the other entities in our ontology.
And if the status of thoughts is not divisive enough, consider
the academic blood spilt over intentionality, consciousness, qualia,
and the "frame problem". Contemplate, in contrast, the
situation in physics. No one (to my knowledge) ever disagreed
that explanations of falling bodies and the movements of the planets
were among the goals of physics. In psychology, however, there
is no similar consensus, in no small measure because the entities
of psychology do not seem to be physical in any straightforward
way (in spite of repeated efforts to "reduce" them to
behavior, neural activity, and the like). This is not to claim
that such a reduction or identification is impossible in principle;
just that it has never been successfully achieved to any appreciable
degree.

To this extent, it is unlikely that any AI program--no
matter how clever, no matter how fascinatingly human-like in its
behavior--could ever, in the current climate, gain consensus as
an example of true intelligence. By those who disagreed with its
basic psychological building blocks, it would be dismissed as
a contrivance; a machine that is very cleverly programmed, but
that cannot be seriously countenanced as an actual instance
of real psychological functioning. This state of affairs is highly
reminiscent of Kuhn's (1970) description of pre-paradigmatic "schools
of thought". The members of the various schools consider
different phenomena to be "basic", and invoke different
theoretical vocabularies in order to explain these phenomena.
Consequently, they have little to discuss with each other.

This is not to endorse Kuhn's broader view of science. Neither
is it to suggest that all ontological questions must be answered
before science can begin its work. But surely there must be some
consensus on ontological matters before any theory --computerized
or no--can hope to gain broad support. Cognitive science has not
yet met this criterion.

3.5. What's the alternative to AI?

So, if AI computer programs are a bad methodology for cognitive
science, as Fodor claims, what is better? In particular, what
would Fodor give us instead? Cognitive scientists, I think, often
forget that, prima facie, there is no more reason for psychologists
to be interested in computers per se than they are in,
say, toasters. The computational model has taken hold of psychology
for specific reasons; reasons that, although they hold promise,
are far from having been fully established.

These reasons are, perhaps, most clearly put forward by Fodor
(1992) himself in an article intended for popular consumption
in the Times Literary Supplement. There he argues that
psychology has, in essence, three problems of long standing that
require resolution before any real progress can be seriously expected:
consciousness, intentionality, and rationality. About the first
he simply says (Dennett's (1991b) recent attempt notwithstanding),
"Nobody has the slightest idea how anything material could
be conscious. Nobody even knows what it would be like to have
the slightest idea about how anything material could be conscious.
So much for the philosophy of consciousness" (1992, p. 5).
Concerning the question of intentionality he is slightly more
optimistic, but notes that it is really a question of semantics,
rather than of philosophy of mind, though he admits it to be sometimes
difficult to know which problem one is working on at a given instant.

The question of rationality, however, is where Fodor believes
a big breakthrough may have come, viz., in the form of
the Turing machine. He writes:

Having noticed [the] parallelism between thoughts and symbols
[viz., that they are both intentional, though we don't
exactly understand how], Turing went on to have the following
perfectly stunning idea. "I'll bet", Turing (more or
less) said, "that one could build a symbol manipulating
machine whose changes of state are driven by the material
properties of the symbols on which they operate (for example,
by their weight, or their shape, or their electrical conductivity).
And I'll bet one could so arrange things that these state changes
are rational in the sense that, given a true symbol to play with,
the machine will reliably convert it into other symbols that are
also true." (1992, p. 6)

Such a system would be rational, at least in the sense that it
would preserve truth from premises to conclusions. This is no
small matter. If John Haugeland's (1985, chapter 1) introductory
historical account is to be believed, this was no less than the
essence of the psychological quests of Hobbes, Descartes, Hume,
and a host of others.

Moreover, I suggest that this is really the only reason,
at present, that computers are of significant interest to psychology,
and that if they fail at this task (i.e., if the parallel
turns out to be only an analogy, and not an identity) computers
are of no more intrinsic interest to psychology than are
calculators, typewriters, and pencils. I don't think, however--and
apparently neither does Fodor--that questions of this sort (i.e.,
Can a machine be conscious? intentional? rational?) are going
to be answered by simply writing programs. There is no question
that one can write programs that mimic rationality, at
least in a limited domain. The way to distinguish between machines
that merely mimic rationality, and those that really have it,
however, does not involve just writing more programs. It is done
by engaging in some serious conceptual analysis about what is
constitutive of true rationality. Once that question is answered,
the question of actually building a machine to those specifications
is practically trivial.

I have often found it useful to ask myself, in evaluating the
claims made for one or another computer program by some member
of the artificial intelligentsia, whether the individual involved
would continue to be interested in psychological matters should
there come forward a definitive proof that minds are not in any
deep way like computers. If the answer is "probably not"
then there is a conflict of interests that should be taken into
account. In such a context, ringing proclamations that brains
are "just information processors", and admonitions that
"there's no need to get mystical (read: "philosophical")
about minds", are to be understood as ideologies,
not as discoveries. For such people, if cognition is not just
computation, then it's not really worth talking about. Some of
us, however, will continue to be interested in cognition even
if it turns out to be something altogether different from computation.

Thus, the proper response to the question, "What would Fodor
give us instead of AI?" is, presumably, "Exactly what
he has given us over the last quarter century--an extended discussion
of, and debate about, the sorts of phenomena that must be accounted
for by any widely acceptable theory of psychology, and of whether
and how those phenomena might be, in principle, instantiated in
a computational system." It comes down to a question of whether
we have yet done enough conceptual analysis to have a good enough
idea of what we are talking about to start building machines.
Cognitive machines would be the crowning glory of cognitive science;
the proof that indeed we had the right theory of cognition. That
building machines now is the way to achieve that goal,
however, is a claim that it is permissible to doubt. Consider
a comparison with the building of automobiles. The theory of the
internal combustion engine came first. After that, the actual
construction was, more or less, a trivial matter. Nearly half
a century of AI does not seem to have brought about the same progress
as did the first half century of building cars. Compare, for instance,
the Indy 500--even of the 1930s--to the Loebner competition (at
which computer programs are submitted to a highly constrained
version the Turing Test).

4. Conclusion.

I have argued that Fodor's analogy is correct, at least in certain
significant details. This assessment has been based on the discovery
of significant similarities in the problems confronted by the
investigator of an Ideal Disneyland and the interrogator in the
Turing Test. These included the claim that indistinguishability
depends on an arbitrary criterion of "normal" investigative
actions, and that the interpretations that one makes of one's
discoveries are, in part, a function of one's previous knowledge
of the thing being investigated. I have also argued that AI's
attempt to escape from the latter of these two problems, by way
of borrowing a page from the book of conceptual-role semantics,
is of dubious validity, and that even a reversion to the "weak
AI" assertion that programs are just theories of cognition
(rather than actual instantiations of it) will not save the day.

Twenty-five years ago Fodor (1968) claimed that there was "something
deeply (i.e., conceptually) wrong with psychology"
(p. vii). Those deep conceptual difficulties remain with
us, and, tedious as it may seem to some, until they are sorted
out I see little hope that a machine is going to come along either
to solve or to dissolve them. Each program will be dedicated,
either explicitly or implicitly, to a certain set of basic psychological
entities, and to certain explications of psychological processes.
Those choices, more than the behavior of the program will determine
who buys in and who sells off. This is not just ideological prejudice,
but a reflection of the fact that, in the final analysis, behavior
is not all that we are interested in. Internal structure and function
are just as important, perhaps moreso.

Despite repeated attempts over the last century to declare philosophy
irrelevant, archaic, dead, or otherwise unpleasantly aromatic,
it is still very much with us in psychology. Machines come and
machines go but the same conceptual problems that dogged Skinner,
Hull, and Watson; Heidegger, Husserl, and Brentano; Hume, Berkeley
and Locke; Kant, Leibniz, and Descartes are with us still. In
effect AI is the attempt to explicitly build an instance of something
that is, at present, not at all well understood, and I judge the
odds of success to be about on the same order as the odds of a
toddler rediscovering the architectural principles governing the
dome, while playing with building blocks.

Footnotes

1.In fact, all CF is "strong." Any version of CF that
didn't take mental states to be just computational functions wouldn't
be CF at all.

2. Harnad (1992) has emphatically argued that the Turing Test
was not intended to be simply an illustrative game, but an actual
scientific criterion of intelligence. Although I agree with Harnad's
analysis of the status of the Turing Test, I believe it still
to be an open question whether it is the right criterion.
Others, most notably Searle (1992, p. 73), have argued strongly
that a strictly behavioral criterion such as the Turing Test is
not sufficient; viz., that concerns such as the physical
structure of the entity in question matter as well.

3. Even Harnad's insistence on indistinguishability "in
principle, for a lifetime" (1992, p. 9) is not sufficient.
In principle, it must be forever, but more about this below.

4. Harnad (1989) is quite explicit about this assumption in his
discussion of the "convergence argument."

5. Notice that even this conclusion is by no means assured.
It will not hold true under more complex encryption schemes than
"replace each letter by another letter." Precisely
such schemes were faced by Turing when he was engaged by the British
military to crack the German "enigma" code during World
War II.